Visual InformaticsPub Date : 2023-09-01DOI: 10.1016/j.visinf.2023.05.004
I Wayan Agus Surya Darma , Nanik Suciati , Daniel Siahaan
{"title":"CARVING-DETC: A network scaling and NMS ensemble for Balinese carving motif detection method","authors":"I Wayan Agus Surya Darma , Nanik Suciati , Daniel Siahaan","doi":"10.1016/j.visinf.2023.05.004","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.05.004","url":null,"abstract":"<div><p>Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs, each representing the values adopted by the Balinese people. Detection of Balinese carving motifs is challenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance, and tiny-size carving motifs. This research aims to improve carving motif detection performance on challenging Balinese carving motifs detection task through a modification of YOLOv5 to support a digital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinese carving detection method consisting of three steps. First, the data generation step performs data augmentation and annotation on Balinese carving images. Second, we proposed a network scaling strategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the model ensemble to generate the most optimal predictions. The ensemble model utilizes NMS to produce higher performance by optimizing the detection results based on the highest confidence score and suppressing other overlap predictions with a lower confidence score. Third, performance evaluation on scaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conserving the cultural heritage and as a reference for other researchers. In addition, this study proposed a novel Balinese carving dataset through data collection, augmentation, and annotation. To our knowledge, it is the first Balinese carving dataset for the object detection task. Based on experimental results, CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 3","pages":"Pages 1-10"},"PeriodicalIF":3.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49731814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-09-01DOI: 10.1016/j.visinf.2023.06.006
Liang Yuan, Issei Fujishiro
{"title":"Multiview SVBRDF capture from unified shape and illumination","authors":"Liang Yuan, Issei Fujishiro","doi":"10.1016/j.visinf.2023.06.006","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.06.006","url":null,"abstract":"<div><p>This paper proposes a stable method for reconstructing spatially varying appearances (SVBRDFs) from multiview images captured under casual lighting conditions. Unlike flat surface capture methods, ours can be applied to surfaces with complex silhouettes. The proposed method takes multiview images as inputs and outputs a unified SVBRDF estimation. We generated a large-scale dataset containing the multiview images, SVBRDFs, and lighting appearance of vast synthetic objects to train a two-stream hierarchical U-Net for SVBRDF estimation that is integrated into a differentiable rendering network for surface appearance reconstruction. In comparison with state-of-the-art approaches, our method produces SVBRDFs with lower biases for more casually captured images.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 3","pages":"Pages 11-21"},"PeriodicalIF":3.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49708296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-07-17DOI: 10.1016/j.visinf.2023.07.002
Reint Jansen , Frida Ruiz Mendoza , William Hurst
{"title":"Augmented reality for supporting geo-spatial planning: An open access review","authors":"Reint Jansen , Frida Ruiz Mendoza , William Hurst","doi":"10.1016/j.visinf.2023.07.002","DOIUrl":"10.1016/j.visinf.2023.07.002","url":null,"abstract":"<div><p>Augmented reality is gaining traction across many domains. One of these is participation within geo-spatial planning projects. The interactive and three-dimensional nature of augmented reality is suitably placed to cater for a higher quality of communication and information exchange in planning processes. Thus, this research provides an overview of the use of AR in planning processes, specifically regarding the participation aspect, through an open-access systematic literature review, for which the investigation identifies 35 articles concerning the current state-of-the-art of augmented reality in planning. Findings indicate the rather limited use of augmented reality in the overall planning process due to technical limitations. Nonetheless, it shows to be a useful technology where it allows for higher user engagement and a clearer understanding among users in planning projects. Additionally, in participation, the technology offers a motivational solution and creates an overall higher acceptance and awareness of the plan, making the participants more engaged and represented in the planning process.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 4","pages":"Pages 1-12"},"PeriodicalIF":3.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X23000360/pdfft?md5=490153cba97e8e194080ec3bb1c39e03&pid=1-s2.0-S2468502X23000360-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85988098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-06-01DOI: 10.1016/j.visinf.2023.01.003
Sérgio M. Rebelo, Tiago Martins, Diogo Ferreira, Artur Rebelo
{"title":"Towards the automation of book typesetting","authors":"Sérgio M. Rebelo, Tiago Martins, Diogo Ferreira, Artur Rebelo","doi":"10.1016/j.visinf.2023.01.003","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.01.003","url":null,"abstract":"<div><p>This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 2","pages":"Pages 1-12"},"PeriodicalIF":3.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49709963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-06-01DOI: 10.1016/j.visinf.2023.04.001
Luc-Etienne Pommé, Romain Bourqui, Romain Giot, Jason Vallet, David Auber
{"title":"NetPrune: A sparklines visualization for network pruning","authors":"Luc-Etienne Pommé, Romain Bourqui, Romain Giot, Jason Vallet, David Auber","doi":"10.1016/j.visinf.2023.04.001","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.04.001","url":null,"abstract":"<div><p>Current deep learning approaches are cutting-edge methods for solving classification tasks. Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used. Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint. In that paper, we present a novel visual analytics approach to interactively prune those networks and thus limit that issue. Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable. We assess the well- founded of our approach through two realistic case studies and a user study. For both of them, the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 2","pages":"Pages 85-99"},"PeriodicalIF":3.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49709998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-06-01DOI: 10.1016/j.visinf.2023.02.001
Linping Yuan , Boyu Li , Siqi Li , Kam Kwai Wong , Rong Zhang , Huamin Qu
{"title":"Tax-Scheduler: An interactive visualization system for staff shifting and scheduling at tax authorities","authors":"Linping Yuan , Boyu Li , Siqi Li , Kam Kwai Wong , Rong Zhang , Huamin Qu","doi":"10.1016/j.visinf.2023.02.001","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.02.001","url":null,"abstract":"<div><p>Given a large number of applications and complex processing procedures, how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities. The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support, but it is unclear how to properly leverage the historical data. To investigate the problem, this study adopts a user-centered design approach. We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems. Then, we propose Tax-Scheduler, an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios. To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations, we conduct user interviews with tax managers and distill several implications for future system design.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 2","pages":"Pages 30-40"},"PeriodicalIF":3.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-06-01DOI: 10.1016/j.visinf.2023.05.001
Julien Klaus, Mark Blacher, Andreas Goral, Philipp Lucas, Joachim Giesen
{"title":"A visual analytics workflow for probabilistic modeling","authors":"Julien Klaus, Mark Blacher, Andreas Goral, Philipp Lucas, Joachim Giesen","doi":"10.1016/j.visinf.2023.05.001","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.05.001","url":null,"abstract":"<div><p>Probabilistic programming is a powerful means for formally specifying machine learning models. The inference engine of a probabilistic programming environment can be used for serving complex queries on these models. Most of the current research in probabilistic programming is dedicated to the design and implementation of highly efficient inference engines. Much less research aims at making the power of these inference engines accessible to non-expert users. Probabilistic programming means writing code. Yet many potential users from promising application areas such as the social sciences lack programming skills. This prompted recent efforts in synthesizing probabilistic programs directly from data. However, working with synthesized programs still requires the user to read, understand, and write some code, for instance, when invoking the inference engine for answering queries. Here, we present an interactive visual approach to synthesizing and querying probabilistic programs that does not require the user to read or write code.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 2","pages":"Pages 72-84"},"PeriodicalIF":3.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49709997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-06-01DOI: 10.1016/j.visinf.2023.02.002
Yuxiao Li , Xinhong Li , Siqi Shen , Longbin Zeng , Richen Liu , Qibao Zheng , Jianfeng Feng , Siming Chen
{"title":"DTBVis: An interactive visual comparison system for digital twin brain and human brain","authors":"Yuxiao Li , Xinhong Li , Siqi Shen , Longbin Zeng , Richen Liu , Qibao Zheng , Jianfeng Feng , Siming Chen","doi":"10.1016/j.visinf.2023.02.002","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.02.002","url":null,"abstract":"<div><p>The digital twin brain (DTB) computing model from brain-inspired computing research is an emerging artificial intelligence technique, which is realized by a computational modeling approach of hardware and software. It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain. Given that the task of the DTB is to simulate the functions of the human brain, comparing the similarities and differences between the two is crucial. However, the visualization study of the DTB is still under-researched. Moreover, the complexity of the datasets (multilevel spatiotemporal granularity and different types of comparison tasks) presents new challenges to the analysis and exploration of visualization. Therefore, in this study, we proposed DTBVis, a visual analytics system that supports comparison tasks for the DTB. DTBVis supports iterative explorations from different levels and at different granularities. Combined with automatic similarity recommendation, and high-dimensional exploration, DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain, thus helping them adjust their model and enhance its functionality. The highest level of DTBVis shows an overview of the datasets from the brain, which is used for comparison and exploration of the function and structure of the DTB and the human brain. The medium level is used for the comparison and exploration of a designated brain region. The low level can analyze a designated brain voxel. We worked closely with experts of brain science and held regular seminars with them. Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 2","pages":"Pages 41-53"},"PeriodicalIF":3.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual InformaticsPub Date : 2023-06-01DOI: 10.1016/j.visinf.2023.05.003
Zeyang Zhou , Zhiyong Yang , Shan Jiang , Tao Zhu , Shixing Ma , Yuhua Li , Jie Zhuo
{"title":"Design and validation of a navigation system of multimodal medical images for neurosurgery based on mixed reality","authors":"Zeyang Zhou , Zhiyong Yang , Shan Jiang , Tao Zhu , Shixing Ma , Yuhua Li , Jie Zhuo","doi":"10.1016/j.visinf.2023.05.003","DOIUrl":"https://doi.org/10.1016/j.visinf.2023.05.003","url":null,"abstract":"<div><h3>Purpose:</h3><p>This paper aims to develop a navigation system based on mixed reality, which can display multimodal medical images in an immersive environment and help surgeons locate the target area and surrounding important tissues precisely.</p></div><div><h3>Methods:</h3><p>To be displayed properly in mixed reality, medical images are processed in this system. High-quality cerebral vessels and nerve fibers with proper colors are reconstructed and exported to mixed reality environment. Multimodal images and models are registered and fused, extracting their key information. The multiple processed images are fused with the real patient in the same coordinate system to guide the surgery.</p></div><div><h3>Results:</h3><p>The multimodal image system is designed and validated properly. In phantom experiments, the average error of preoperative registration is 1.003 mm and the standard deviation is 0.096 mm. The average proportion of well-registered areas is 94.9%. In patient experiments, the surgeons who participated in the experiments generally indicated that the system had excellent performance and great application prospect for neurosurgery.</p></div><div><h3>Conclusion:</h3><p>This article proposes a navigation system of multimodal images for neurosurgery based on mixed reality. Compared with other navigation methods, this system can help surgeons locate the target area and surrounding important tissues more precisely and rapidly.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"7 2","pages":"Pages 64-71"},"PeriodicalIF":3.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49709995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}